This paper presents methods for extracting structured information from invoice documents and proposes a set of evaluation metrics (EM) to assess the accuracy of the extracted data against annotated ground truth. The approach involves pre-processing scanned or digital invoices, applying Docling and LlamaCloud Services to identify and extract key fields such as invoice number, date, total amount, and vendor details. To ensure the reliability of the extraction process, we establish a robust evaluation framework comprising field-level precision, consistency check failures, and exact match accuracy. The proposed metrics provide a standardized way to compare different extraction methods and highlight strengths and weaknesses in field-specific performance.
@article{arxiv.2510.15727,
title = {Invoice Information Extraction: Methods and Performance Evaluation},
author = {Sai Yashwant and Anurag Dubey and Praneeth Paikray and Gantala Thulsiram},
journal= {arXiv preprint arXiv:2510.15727},
year = {2025}
}